Data Scientist

Asenium Consulting; Brussel; Hybride; EUR 70 000 - 110 000; Plein temps

Résumé du poste

Asenium Consulting is seeking a Data Scientist to join an AI team in Brussels for a 16-month fraud detection project. This role operates under a 50% hybrid work model with focus on production-grade AI applications and advanced fraud detection solutions.

The start date is as soon as possible, and the team collaborates with Fraud Operations, Risk, Compliance and Product to tackle evolving fraud trends.

Qualifications

• 7+ years of experience in data science with a fraud-detection focus.
• 5+ years of experience developing AI applications.
• Strong hands-on experience with Python, Pandas and SQL.
• Extensive experience with Spark, Hadoop and distributed data processing.
• Experience collecting, enriching, structuring and validating complex transactional and behavioral data.
• Experience with supervised and unsupervised ML.
• Experience with anomaly detection, graph analytics, network analysis and behavioral analytics.
• Proven ability to deploy data-centric AI solutions in production.

Responsabilités

• Collect, label, curate, structure, and engineer data to improve fraud detection models.
• Design, develop, and improve fraud detection models using ML, graph analytics, anomaly detection, and behavioral analytics.
• Transform large volumes of transactional and customer data into actionable fraud intelligence.
• Perform feature engineering, data enrichment, and pattern discovery.
• Collaborate with Fraud Operations, Risk, Compliance, and Product teams to identify emerging fraud trends.
• Translate business needs into scalable analytical solutions.
• Investigate complex fraud schemes and uncover new attack vectors.
• Develop detection strategies to mitigate financial and reputational risks.
• Contribute to deploying AI applications in production.

Connaissances

Python

Pandas

SQL

Machine learning

Fraud detection

Data engineering

Supervised learning

Unsupervised learning

Graph analytics

Anomaly detection

Behavioral analytics

Pattern discovery

Production AI

Outils

Spark

Hadoop

Distributed data processing

Description du poste

We are looking for an Data Scientist to join an AI team in Brussels for a 16-month fraud detection project. The role follows a 50% hybrid work model and focuses on developing production-grade AI applications and advanced fraud detection solutions. The start date is as soon as possible.

Key responsibilities

• Collect, label, curate, structure, and engineer data to improve fraud detection models.
• Design, develop, and continuously improve fraud detection models using machine learning, graph analytics, anomaly detection, and behavioral analytics.
• Transform large volumes of transactional and customer data into actionable fraud intelligence.
• Perform feature engineering, data enrichment, and pattern discovery.
• Collaborate with Fraud Operations, Risk, Compliance, and Product teams to identify emerging fraud trends.
• Translate business needs into scalable analytical solutions.
• Investigate complex fraud schemes and uncover new attack vectors.
• Develop detection strategies to mitigate financial and reputational risks.
• Contribute to deploying AI applications in production.

Must-Have Requirements

• You have at least 7 years of experience in data science, with a focus on fraud detection.
• You have at least 5 years of experience developing AI applications.
• You have strong hands-on experience with Python, Pandas, and SQL.
• You have strong experience processing large-scale datasets with Spark, Hadoop, and distributed data processing technologies.
• You have experience collecting, enriching, structuring, and validating complex transactional and behavioral data.
• You have experience with supervised and unsupervised machine learning.
• You have experience with anomaly detection, graph analytics, network analysis, and behavioral analytics.
• You have experience building data-centric AI solutions using robust software engineering practices.
• You have experience deploying AI applications in production.